{"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"gpuClass":"standard","kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":7571253,"sourceType":"datasetVersion","datasetId":4407676},{"sourceId":7678915,"sourceType":"datasetVersion","datasetId":4479814},{"sourceId":7713636,"sourceType":"datasetVersion","datasetId":4504654},{"sourceId":8017122,"sourceType":"datasetVersion","datasetId":4723613},{"sourceId":4298,"sourceType":"modelInstanceVersion","modelInstanceId":3093},{"sourceId":5110,"sourceType":"modelInstanceVersion","modelInstanceId":3898}],"dockerImageVersionId":30674,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<h2><center><font color=#D40004><u>Benchmark 2: Openchat-3.5-1210 and dolphin-2.2.1-mistral-7b </u></font></center></h2>\n","metadata":{}},{"cell_type":"markdown","source":"<div style=\"padding: 40px; background: linear-gradient(135deg, #f5f7fa, #cdd2d8); border: 3px groove #d1d8e0; border-radius: 30px; box-shadow: 0 10px 25px rgba(0,0,0,0.1); font-size: 120%; line-height: 1.9; color: #333; font-family: 'Georgia', serif; text-align: justify; position: relative;\">\n    <h2 style=\"color: #2c3e50; font-size: 150%; border-bottom: 3px solid #3498db; display: inline-block; padding-bottom: 10px; margin-bottom: 20px;\">\n        Notebook Gool\n    </h2>\n    <p style=\"font-size: 140%; color: #34495e; letter-spacing: 1px;\">T\nThe objective of this notebook is to evaluate the performance of Openchat-3.5-1210 and dolphin-2.2.1-mistral-7b using the Table-extract Benchmark dataset available at <a href=\"https://huggingface.co/datasets/Effyis/Table-Extraction\" style=\"color: #fffff; text-decoration: none;\">Hugging Face.</a></p>\n</div>\n","metadata":{}},{"cell_type":"markdown","source":"# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:center; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'></span></b> <b>Table of Content</b></div>\n\n* [I. Loading and Importing Libraries](#1)\n* [II. Definition and Implementation of Metrics](#2)\n* [III. Clean Response Obtained by LLM](#3)\n* [IV. Prepare Data for Benchmarking](#5)\n* [V. Benchmark](#6)\n  * [Openchat-3.5-1210](#7)\n  * [dolphin-2.2.1-mistral-7b](#8)\n  ","metadata":{}},{"cell_type":"markdown","source":"<a id='1'></a>\n# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>I |</span></b> <b>Loading and Importing Libraries</b></div>\n","metadata":{}},{"cell_type":"code","source":"%%capture\n!pip install --upgrade pip\n!pip install bitsandbytes\n!pip install transformers","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:06:29.909276Z","iopub.execute_input":"2024-04-07T13:06:29.909660Z","iopub.status.idle":"2024-04-07T13:09:02.668714Z","shell.execute_reply.started":"2024-04-07T13:06:29.909627Z","shell.execute_reply":"2024-04-07T13:09:02.667305Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import re\nimport json\nfrom tqdm import tqdm\nimport pandas as pd\nfrom datasets import load_dataset, Dataset\nfrom wand.image import Image as WImage\nimport torch\nimport time \nfrom transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:02.671378Z","iopub.execute_input":"2024-04-07T13:09:02.671835Z","iopub.status.idle":"2024-04-07T13:09:17.144274Z","shell.execute_reply.started":"2024-04-07T13:09:02.671788Z","shell.execute_reply":"2024-04-07T13:09:17.143391Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"<a id='2'></a>\n# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>II |</span></b> <b>Definition and Implementation of Metrics</b></div>\nSo, let's begin by providing an example of the expected output.","metadata":{}},{"cell_type":"code","source":"desired_output = {\n    \"GeographicContext\": \"Intercountry\",\n    \"SubGeographicContext\": \"Intra-Western\",\n    \"Channel\": \"purchase at ATM\",\n    \"RateType\": \"fallback fee rates\",\n    \"Notes\":[\"Maestro MO/TO is permitted only for refund transactions.\",\n\"For transactions less than or equal to EUR 25 only. Transactions greater than EUR 25 are processed under normal Maestro acceptance criteria.\"],\n    \n    \"Rates\": [\n        {\n            \"PaymentProduct\": \"Maestro Prepaid Commercial\",\n            \"Details\": [\n                {\n                    \"FeeTier\": \"Chip\",\n                    \"IRD\": [\"45\"],\n                    \"Rate\": \"0.40% + EUR 0.05\"\n                },\n                {\n                    \"FeeTier\": \"Chip: Late Presentment\",\n                    \"IRD\": [\"45\"],\n                    \"Rate\": \"0.75% + EUR 0.05\"\n                },\n                {\n                    \"FeeTier\": \"PIN Verified\",\n                    \"IRD\": [\"46\"],\n                    \"Rate\": \"0.50% + EUR 0.05\"\n                },\n                {\n                    \"FeeTier\": \"Signature Verified\",\n                    \"IRD\": [\"47\"],\n                    \"Rate\": \"0.75% + EUR 0.05\"\n                },\n                {\n                    \"FeeTier\": \"Masterpass Wallet\",\n                    \"IRD\": [\"PW\"],\n                    \"Rate\": \"1.50%\"\n                },\n                {\n                    \"FeeTier\": \"Secure E-commerce\",\n                    \"IRD\": [\"48\"],\n                    \"Rate\": \"1.05% + EUR 0.05\"\n                },\n                {\n                    \"FeeTier\": \"Mail Order/Telephone Order\",\n                    \"IRD\": [\"MO\"],\n                    \"Rate\": \"1.05% + EUR 0.05\"\n                },\n                {\n                    \"FeeTier\": \"Contactless\",\n                    \"IRD\": [\"P1\"],\n                    \"Rate\": \"0.40% + EUR 0.05\"\n                }\n            ]\n        },\n        {\n            \"PaymentProduct\": \"Maestro\",\n            \"Details\": [\n                {\n                    \"FeeTier\": \"Purchase part of the Purchase with Cash Back transaction\",\n                    \"IRD\": [],\n                    \"Rate\": \"Depending on tier claimed POS interchange rate relevant to the applicable tier. Refer to Intra-EEA Maestro POS Fallback Interchange Fee Rates.\"\n                },\n                {\n                    \"FeeTier\": \"Cash back part of the Purchase with Cash Back transaction\",\n                    \"IRD\": [\"N/A\"],\n                    \"Rate\": \"0.00\"\n                }\n            ]\n        }\n    ]\n}\n","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.145431Z","iopub.execute_input":"2024-04-07T13:09:17.145928Z","iopub.status.idle":"2024-04-07T13:09:17.157159Z","shell.execute_reply.started":"2024-04-07T13:09:17.145900Z","shell.execute_reply":"2024-04-07T13:09:17.156027Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"To compare between the expected JSON and the predicted JSON, we first need to verify the percentage of predicted keys relative to the desired keys","metadata":{}},{"cell_type":"markdown","source":">##  Percentage of predicted keys","metadata":{}},{"cell_type":"markdown","source":"Let's begin by defining a function to retrieve all keys from a dictionary.","metadata":{}},{"cell_type":"code","source":"def get_keys(d):\n    # Iterate over each key-value pair in the dictionary\n    for k, v in d.items():\n        # Append the key to the list of all_keys\n        all_keys.append(k)\n        # If the value is a dictionary, recursively call get_keys\n        if isinstance(v, dict):\n            get_keys(v)\n        # If the value is a list, iterate over each item\n        elif isinstance(v, list):\n            for item in v:\n                # If the item is a dictionary, recursively call get_keys\n                if isinstance(item, dict):\n                    get_keys(item)\n# Define a function to retrieve all unique keys from a nested dictionary\ndef get_all_keys(d):\n    # Declare all_keys as a global variable\n    global all_keys\n    # Initialize all_keys as an empty list\n    all_keys = []\n    # Call the helper function get_keys to populate all_keys\n    get_keys(d)\n    # Return a list containing the unique keys by converting all_keys to a set and then back to a list\n    return list(set(all_keys))","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.159803Z","iopub.execute_input":"2024-04-07T13:09:17.160133Z","iopub.status.idle":"2024-04-07T13:09:17.364090Z","shell.execute_reply.started":"2024-04-07T13:09:17.160085Z","shell.execute_reply":"2024-04-07T13:09:17.363165Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"# Testing our function\nget_all_keys(desired_output)","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.365376Z","iopub.execute_input":"2024-04-07T13:09:17.365713Z","iopub.status.idle":"2024-04-07T13:09:17.377387Z","shell.execute_reply.started":"2024-04-07T13:09:17.365674Z","shell.execute_reply":"2024-04-07T13:09:17.376357Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"['SubGeographicContext',\n 'FeeTier',\n 'Channel',\n 'RateType',\n 'Notes',\n 'Details',\n 'Rate',\n 'Rates',\n 'PaymentProduct',\n 'GeographicContext',\n 'IRD']"},"metadata":{}}]},{"cell_type":"markdown","source":"Now, we define the percentage of predicted keys as follows:\n\n$$\\Large \\text{Percentage of predicted keys} = \\frac{\\text{Number of correctly predicted keys}}{\\text{Total number of true keys}}$$\n","metadata":{}},{"cell_type":"code","source":"def Percentage_of_predicted_keys(true_dic, pred_dic):\n    # Get all keys of the true dictionary\n    all_keys_of_true_dic = get_all_keys(true_dic)\n    # Get all keys of the predicted dictionary\n    all_keys_of_pred_dic = get_all_keys(pred_dic)\n    \n    # Check if there are no keys in the true dictionary to avoid division by zero\n    if len(all_keys_of_true_dic) == 0:\n        return 0  # Avoid division by zero\n    \n    # Initialize count of predicted keys\n    p_keys = 0\n    # Iterate through all keys in the predicted dictionary\n    for key in all_keys_of_pred_dic:\n        # Check if the key is also present in the true dictionary\n        if key in all_keys_of_true_dic:\n            # Increment count if the key is found in both dictionaries\n            p_keys += 1\n    \n    # Calculate the percentage of predicted keys compared to true keys\n    p_keys /= len(all_keys_of_true_dic)\n    # Return the percentage of predicted keys\n    return p_keys","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.378877Z","iopub.execute_input":"2024-04-07T13:09:17.379629Z","iopub.status.idle":"2024-04-07T13:09:17.387401Z","shell.execute_reply.started":"2024-04-07T13:09:17.379596Z","shell.execute_reply":"2024-04-07T13:09:17.386481Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"# Define a true dictionary\ntrue_dict = {\n    \"name\": \"John\",\n    \"age\": 30,\n    \"city\": \"New York\"\n}\n\n# Define a predicted dictionary with some correct and some incorrect keys\npred_dict = {\n    \"name\": \"John\",\n    \"age\": 30,\n    \"country\": \"USA\"\n}\n\n# Calculate the percentage of predicted keys\npercentage = Percentage_of_predicted_keys(true_dict, pred_dict)\n\n# Print the result\nprint(\"Percentage of predicted keys:\", percentage)","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.388792Z","iopub.execute_input":"2024-04-07T13:09:17.389218Z","iopub.status.idle":"2024-04-07T13:09:17.405402Z","shell.execute_reply.started":"2024-04-07T13:09:17.389181Z","shell.execute_reply":"2024-04-07T13:09:17.404411Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Percentage of predicted keys: 0.6666666666666666\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Now we will define the principal metrics used to compare the values of two JSON objects.","metadata":{}},{"cell_type":"markdown","source":">## Percentage of predicted values\n\nThe function calculates the percentage of correctly predicted values compared to the total number of true values across different types of data structures.\n\nThe formula for calculating the percentage of values is as follows:\n\n$$\n\\text{Percentage of values} = \\frac{\\sum_{i=1}^{\\text{Total number of predicted keys}} p_i \\cdot w_i}{\\sum_{i=1}^{\\text{Total number of true keys}} w_i}\n$$\n\nHere, $p_i$ represents the percentage of correctly predicted values for each key. It's calculated as:\n\n$$p_i = \\frac{\\text{Number of correctly predicted values of item i}}{\\text{Total number of true values of item i}}$$\n\nand $ w_i$ is the total number of true values of item i.","metadata":{}},{"cell_type":"code","source":"# Define a function to count the values in a nested dictionary. This function assists in calculating weights.\ndef count_values_in_dic(d):\n    # Initialize a counter\n    count = 0\n    \n    # Check if d is a string\n    if isinstance(d, (str,int,float)):\n        # Increment the counter\n        count += 1\n        \n    # Check if d is a list containing only strings\n    if isinstance(d, list) and all(isinstance(item, (str,int,float)) for item in d):\n        # Add the length of the list to the counter\n        count += len(d)\n        \n    # Check if d is a list containing only dictionaries\n    if isinstance(d, list) and all(isinstance(item, dict) for item in d):\n        # Iterate through each item in the list\n        for item in d:\n            # Recursively call count_values_in_dic for each item in the list\n            count += count_values_in_dic(item)\n  \n    # Check if d is a dictionary\n    if isinstance(d, dict):\n        # Traverse through the dictionary items\n        for key, value in d.items():\n            # If the value is a string, increment the counter\n            if isinstance(value, (str,int,float)):\n                count += 1\n            \n            # If the value is a dictionary, count its values recursively\n            elif isinstance(value, dict):\n                count += count_values_in_dic(value)\n                    \n            # If the value is a list, count the values in the list      \n            elif isinstance(value, list):\n                # Iterate through each element in the list\n                for item in value:\n                    # If the element is a dictionary, count its values\n                    if isinstance(item, dict):\n                        count += count_values_in_dic(item)\n                    # If the element is a string, increment the counter\n                    elif isinstance(item, (str,int,float)):\n                        count += 1\n\n    # Return the total count\n    return count","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.407055Z","iopub.execute_input":"2024-04-07T13:09:17.407389Z","iopub.status.idle":"2024-04-07T13:09:17.418491Z","shell.execute_reply.started":"2024-04-07T13:09:17.407360Z","shell.execute_reply":"2024-04-07T13:09:17.417624Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# Define a sample nested dictionary\nsample_dict = {\n    \"name\": \"John\",\n    \"age\": 30,\n    \"city\": \"New York\",\n    \"friends\": [\n        {\n            \"name\": \"Alice\",\n            \"age\": 28\n        },\n        {\n            \"name\": \"Bob\",\n            \"age\": 32\n        }\n    ],\n    \"hobbies\": [\"reading\", \"traveling\", \"cooking\"]\n}\n\n# Count the values in the dictionary\nvalues_count = count_values_in_dic(sample_dict)\n\n# Print the result\nprint(\"Number of values in the dictionary:\", values_count)","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.419779Z","iopub.execute_input":"2024-04-07T13:09:17.420091Z","iopub.status.idle":"2024-04-07T13:09:17.437586Z","shell.execute_reply.started":"2024-04-07T13:09:17.420064Z","shell.execute_reply":"2024-04-07T13:09:17.436521Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Number of values in the dictionary: 10\n","output_type":"stream"}]},{"cell_type":"code","source":"def calculate_percentage_of_values(true_dic, pred_dic):\n    total_percentage = 0  # Initialize total percentage\n    sum_of_w_i = 0 # Initialize sum of weights\n    \n    # Type 1: Single string values\n    for key, true_value in true_dic.items():  # Loop through key-value pairs in true_dic\n        if isinstance(true_value, str):  # Check if the value is a string\n            w_i = 1 # Weight for this item\n            sum_of_w_i += w_i  # Add weight to the sum of weights\n            # Check if the key exists in pred_dic, if its value is a string and if it matches the true value\n            if key in pred_dic and isinstance(pred_dic[key], str) and pred_dic[key] == true_value:\n                match = 1  # Assign perfect match\n            else:\n                match = 0  # Assign no match\n            total_percentage += match*w_i  # Add the match value to the total percentage\n            \n    # Type 2: Lists of strings\n    for key, true_value in true_dic.items():  # Loop through key-value pairs in true_dic\n        if isinstance(true_value, list) and all(isinstance(item, str) for item in true_value):\n            # Check if the value is a list of strings\n            w_i = len(true_value) # Weight for this item\n            sum_of_w_i += w_i  # Add weight to the sum of weights\n            total_number_of_true_values = len(true_value)  # Get the total number of true values\n            \n            if key in pred_dic and isinstance(pred_dic[key], list):\n                # Check if the key exists in pred_dic and its value is a list\n                correct_pred_values = sum(1 for v in pred_dic[key] if v in true_value)  # Count correct predictions\n            else:\n                correct_pred_values = 0  # No correct predictions if the key doesn't exist or its value is not a list\n            if total_number_of_true_values:\n                percentage_correct = correct_pred_values / total_number_of_true_values  # Calculate percentage of correct predictions\n            else:\n                percentage_correct = 0  # If there are no true values, assign 0\n            total_percentage += percentage_correct*w_i  # Add the percentage to the total percentage\n    \n    # Type 4: dictionarie\n    for key, true_value in true_dic.items():  # Loop through key-value pairs in true_dic\n        if isinstance(true_value, dict)  :\n            w_i = count_values_in_dic(true_value) # Weight for this item\n            sum_of_w_i += w_i # Add weight to the sum of weights\n            \n            if key in pred_dic and isinstance(pred_dic[key], dict):\n                percentage_correct = calculate_percentage_of_values(true_value, pred_dic[key])\n            else:\n                percentage_correct = 0\n            total_percentage += percentage_correct*w_i\n    # Type 5: Lists of dictionaries\n    for key, true_value in true_dic.items():  # Loop through key-value pairs in true_dic\n        if isinstance(true_value, list) and all(isinstance(item, dict) for item in true_value):\n            # Check if the value is a list of dictionaries\n            w_i = count_values_in_dic(true_value) # Weight for this item\n            sum_of_w_i += w_i # Add weight to the sum of weights\n            sub_total_percentage = 0  # Initialize subtotal percentage\n            \n            for dict_item in true_value:  # Loop through each dictionary in the list\n                max_similarity = 0  # Initialize max similarity score\n                most_similar_pred_item = None  # Initialize most similar predicted item\n                pred_items = pred_dic.get(key, [])  # Get predicted items for the current key\n                #print(dict_item)\n                for pred_item in pred_items:  # Loop through each prediction for the current key\n                    \n                    similarity = calculate_percentage_of_values(dict_item, pred_item)  # Calculate similarity\n\n                    if similarity > max_similarity:\n                        max_similarity = similarity\n                        most_similar_pred_item = pred_item\n                    if max_similarity==1:\n                        break\n                if most_similar_pred_item is not None:\n                    pred_items.remove(most_similar_pred_item)  # Remove the most similar predicted item from the list\n                sub_total_percentage += max_similarity  # Add max similarity to subtotal\n                \n            if len(true_value) > 0:\n                average_similarity = sub_total_percentage / len(true_value)  # Calculate average similarity\n            else:\n                average_similarity = 0  # If there are no dictionaries, assign 0\n            total_percentage += average_similarity* w_i  # Add the percentage to the total percentage\n    #print(total_percentage , sum_of_w_i)\n    if sum_of_w_i==0:\n        sum_of_w_i=1\n    return total_percentage / sum_of_w_i  # Calculate and return the average percentage","metadata":{"execution":{"iopub.status.busy":"2024-04-07T15:11:49.088303Z","iopub.execute_input":"2024-04-07T15:11:49.088720Z","iopub.status.idle":"2024-04-07T15:11:49.107255Z","shell.execute_reply.started":"2024-04-07T15:11:49.088685Z","shell.execute_reply":"2024-04-07T15:11:49.106166Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"true_dict = {\n    'a': 'hello',\n    'b': ['world', 'foo', 'bar'],\n    'c': [{'x': 'apple'}, {'y': 'banana'}, {'z': 'orange'}],\n    'd': {\n        'e': 'python',\n        'f': ['java', 'javascript'],\n        'g': [{'h': 'ruby'}, {'i': 'php'}]\n    }\n}\n\npred_dict = {\n    'a': 'Hey',\n    'b': ['world', 'foo', 'bar'],\n    'c': [{'x': 'apple'}, {'y': 'banana'}, {'z': 'orange'}],\n    'd': {\n        'e': 'python',\n        'f': ['java', 'javascript'],\n        'g': [{'h': 'ruby'}, {'i': 'php'}]\n    }\n}\n\n# Calculate the percentage of predicted values\npercentage = calculate_percentage_of_values(true_dict, pred_dict)\n\n# Print the result\nprint(\"Percentage of predicted values:\", percentage)","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.465623Z","iopub.execute_input":"2024-04-07T13:09:17.466031Z","iopub.status.idle":"2024-04-07T13:09:17.484475Z","shell.execute_reply.started":"2024-04-07T13:09:17.465993Z","shell.execute_reply":"2024-04-07T13:09:17.483311Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Percentage of predicted values: 0.9166666666666666\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id='3'></a>\n# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>III |</span></b> <b>Clean Response Obtained by LLM</b></div>\n\nOur goal is to obtain a response from LLM in JSON format. However, LLMs sometimes provide additional explanatory sentences along with the JSON data. Therefore, we need to define a function to extract only the JSON data from the response.\n\nHere example of our issue:\n![image.png](attachment:45c74a10-7e88-47e2-ae31-ef8bb0f46558.png)","metadata":{},"attachments":{"45c74a10-7e88-47e2-ae31-ef8bb0f46558.png":{"image/png":"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"}}},{"cell_type":"code","source":"def clean_and_load_json(s):\n    # Remove comments\n    s = re.sub(r'//.*?\\n', '', s)\n    s = re.sub(r'#.*?\\n', '', s) \n    # Remove trailing commas before closing brackets in lists and dictionaries\n    s = re.sub(r',\\s*\\n\\s*(\\]|\\})', r'\\1', s)\n    # Remove patterns like '\\n       ...\\n'\n    s = re.sub(r'\\n\\s*\\.\\.\\.\\n', '', s)\n    # Remove comma before } or ]\n    s = re.sub(r',\\s*(\\]|\\})', r'\\1', s)\n    # Remove unnecessary whitespace\n    s = s.strip()\n    # Load the cleaned JSON string\n    print(s)\n    return json.loads(s)","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.485692Z","iopub.execute_input":"2024-04-07T13:09:17.486053Z","iopub.status.idle":"2024-04-07T13:09:17.497066Z","shell.execute_reply.started":"2024-04-07T13:09:17.486020Z","shell.execute_reply":"2024-04-07T13:09:17.495759Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"def parse_json(s):\n    s = s.replace('{\"message\": \"Context lacks a Payment product,FeeTier and Rate\"}','')\n    if '{' in s:    \n        try:\n            json_str = json.loads(s)\n        except:\n            # Find the index of the first occurrence of '{\\n'\n            start_idx = s.find('{')\n\n            # Find the index of the last occurrence of '\\n}'\n            end_idx = s.rfind('}')\n\n            # If either index is not found, raise an error\n            if start_idx == -1 or end_idx == -1:\n                return {\"message\": \"Context lacks a Payment product,FeeTier and Rate\"}\n\n            # Extract the JSON substring from start_idx to end_idx (inclusive) + 3 to include the closing '\\n}'\n            json_str = s[start_idx:end_idx+1]\n            try:\n                 json_str = json.loads(json_str)\n            except:\n                json_str =  clean_and_load_json(json_str)\n    else:\n        return {\"message\": \"Context lacks a Payment product,FeeTier and Rate\"}\n    return json_str","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.498732Z","iopub.execute_input":"2024-04-07T13:09:17.499154Z","iopub.status.idle":"2024-04-07T13:09:17.513647Z","shell.execute_reply.started":"2024-04-07T13:09:17.499096Z","shell.execute_reply":"2024-04-07T13:09:17.512513Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"<a id='5'></a>\n# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>IV |</span></b> <b>Data Preparation</b></div>\n","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/table-extraction/table_extract.csv\")\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:17.514959Z","iopub.execute_input":"2024-04-07T13:09:17.516472Z","iopub.status.idle":"2024-04-07T13:09:19.138197Z","shell.execute_reply.started":"2024-04-07T13:09:17.516425Z","shell.execute_reply":"2024-04-07T13:09:19.137164Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"                                             context  \\\n0                    aircraft                    ...   \n1    order year manufacturer                  mod...   \n2                player       no    nationality  ...   \n3               player  no              nationali...   \n4                player       no    nationality  ...   \n\n                                              answer  \n0  {\"aircraft\":{\"0\":\"robinson r - 22\",\"1\":\"bell 2...  \n1  {\"order year\":{\"0\":\"1992 - 93\",\"1\":\"1996\",\"2\":...  \n2  {\"player\":{\"0\":\"quincy acy\",\"1\":\"hassan adams\"...  \n3  {\"player\":{\"0\":\"patrick o'bryant\",\"1\":\"jermain...  \n4  {\"player\":{\"0\":\"mark baker\",\"1\":\"marcus banks\"...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>context</th>\n      <th>answer</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>aircraft                    ...</td>\n      <td>{\"aircraft\":{\"0\":\"robinson r - 22\",\"1\":\"bell 2...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>order year manufacturer                  mod...</td>\n      <td>{\"order year\":{\"0\":\"1992 - 93\",\"1\":\"1996\",\"2\":...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>player       no    nationality  ...</td>\n      <td>{\"player\":{\"0\":\"quincy acy\",\"1\":\"hassan adams\"...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>player  no              nationali...</td>\n      <td>{\"player\":{\"0\":\"patrick o'bryant\",\"1\":\"jermain...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>player       no    nationality  ...</td>\n      <td>{\"player\":{\"0\":\"mark baker\",\"1\":\"marcus banks\"...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df = df.loc[:50]","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:19.139540Z","iopub.execute_input":"2024-04-07T13:09:19.139856Z","iopub.status.idle":"2024-04-07T13:09:19.145146Z","shell.execute_reply.started":"2024-04-07T13:09:19.139827Z","shell.execute_reply":"2024-04-07T13:09:19.144100Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"<a id='6'></a>\n# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>V |</span></b> <b>Benchmark</b></div>\n","metadata":{}},{"cell_type":"code","source":"prompt = \"\"\"Your task is to extract relevant information from the provided context and format it into a JSON object, following the template below.\n A JSON object representing the extracted table structure. The JSON object follows this format: \n { \"column_1\": { \"row_id\": \"val1\", \"row_id\": \"val2\", ... }, \n \"column_2\": { \"row_id\": \"val1\", \"row_id\": \"val2\", ... },\n ... } \n Each key in the JSON object represents a column header, and the corresponding value is another object containing key-value pairs for each row in that column.\n\nINPUT example:\n# do not use the data from the examples & template; they are just for reference only. The following data contains actual information. If a value is not found, leave it empty. \n\n                  aircraft                    description      max gross weight                total disk area                        max disk loading\n0          robinson r - 22       light utility helicopter      1370 lb (635 kg)  497 ft square (46.2 m square)   2.6 lb / ft square (14 kg / m square)\n1     bell 206b3 jetranger  turboshaft utility helicopter     3200 lb (1451 kg)  872 ft square (81.1 m square)   3.7 lb / ft square (18 kg / m square)\n2         ch - 47d chinook        tandem rotor helicopter   50000 lb (22680 kg)  5655 ft square (526 m square)   8.8 lb / ft square (43 kg / m square)\n3              mil mi - 26        heavy - lift helicopter  123500 lb (56000 kg)  8495 ft square (789 m square)  14.5 lb / ft square (71 kg / m square)\n4  ch - 53e super stallion        heavy - lift helicopter   73500 lb (33300 kg)  4900 ft square (460 m square)    15 lb / ft square (72 kg / m square)\n\nOUTPUT example:\n# do not use the data from the examples & template; they are just for reference only. The following data contains actual information. If a value is not found, leave it empty. \n{\n  \"aircraft\": {\n    \"0\": \"robinson r - 22\",\n    \"1\": \"bell 206b3 jetranger\",\n    \"2\": \"ch - 47d chinook\",\n    \"3\": \"mil mi - 26\",\n    \"4\": \"ch - 53e super stallion\"\n  },\n  \"description\": {\n    \"0\": \"light utility helicopter\",\n    \"1\": \"turboshaft utility helicopter\",\n    \"2\": \"tandem rotor helicopter\",\n    \"3\": \"heavy - lift helicopter\",\n    \"4\": \"heavy - lift helicopter\"\n  },\n  \"max gross weight\": {\n    \"0\": \"1370 lb (635 kg)\",\n    \"1\": \"3200 lb (1451 kg)\",\n    \"2\": \"50000 lb (22680 kg)\",\n    \"3\": \"123500 lb (56000 kg)\",\n    \"4\": \"73500 lb (33300 kg)\"\n  },\n  \"total disk area\": {\n    \"0\": \"497 ft square (46.2 m square)\",\n    \"1\": \"872 ft square (81.1 m square)\",\n    \"2\": \"5655 ft square (526 m square)\",\n    \"3\": \"8495 ft square (789 m square)\",\n    \"4\": \"4900 ft square (460 m square)\"\n  },\n  \"max disk loading\": {\n    \"0\": \"2.6 lb / ft square (14 kg / m square)\",\n    \"1\": \"3.7 lb / ft square (18 kg / m square)\",\n    \"2\": \"8.8 lb / ft square (43 kg / m square)\",\n    \"3\": \"14.5 lb / ft square (71 kg / m square)\",\n    \"4\": \"15 lb / ft square (72 kg / m square)\"\n  }\n}\n\"\"\"","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:19.146554Z","iopub.execute_input":"2024-04-07T13:09:19.146870Z","iopub.status.idle":"2024-04-07T13:09:19.156099Z","shell.execute_reply.started":"2024-04-07T13:09:19.146843Z","shell.execute_reply":"2024-04-07T13:09:19.155277Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"def accuracy(df):\n    df['clean_pred_response'] = df['pred_response'].map(parse_json)\n    \n    average_percentage_of_predicted_keys = 0\n    average_percentage_of_predicted_values = 0\n    \n    for i in df.index:\n        true_dic = df.loc[i,'answer']\n        pred_dic = df.loc[i,'clean_pred_response']\n        average_percentage_of_predicted_keys += Percentage_of_predicted_keys(true_dic, pred_dic)\n        average_percentage_of_predicted_values += calculate_percentage_of_values(true_dic, pred_dic)\n\n    average_percentage_of_predicted_keys /= df.shape[0]\n    average_percentage_of_predicted_values /= df.shape[0]\n    \n    return average_percentage_of_predicted_keys,average_percentage_of_predicted_values","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:19.157417Z","iopub.execute_input":"2024-04-07T13:09:19.157783Z","iopub.status.idle":"2024-04-07T13:09:19.173274Z","shell.execute_reply.started":"2024-04-07T13:09:19.157748Z","shell.execute_reply":"2024-04-07T13:09:19.172292Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"<a id='7'></a>\n>## Openchat-3.5-1210","metadata":{}},{"cell_type":"code","source":"base_model_id = \"openchat/openchat-3.5-1210\"\nbnb_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_use_double_quant=True,\n    bnb_4bit_quant_type=\"nf4\",\n    bnb_4bit_compute_dtype=torch.bfloat16\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config, device_map=\"auto\")\ntokenizer = AutoTokenizer.from_pretrained(base_model_id)","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:09:19.174792Z","iopub.execute_input":"2024-04-07T13:09:19.175509Z","iopub.status.idle":"2024-04-07T13:12:44.901610Z","shell.execute_reply.started":"2024-04-07T13:09:19.175466Z","shell.execute_reply":"2024-04-07T13:12:44.900605Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/623 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aea1ac67b1f943b9a91a64830990a990"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"43e246539b584238a67d2ad39aafec47"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7cd6e7939cc2431190f9e20be56832da"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00001-of-00003.safetensors:   0%|          | 0.00/4.94G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c0e1b9f86fdd46908bb749cbbdfdb11e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00002-of-00003.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ecdd245d4f1d428da8a27b85f90e62e7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00003-of-00003.safetensors:   0%|          | 0.00/4.54G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1be3ec2f74bb444599a4ca2babe91634"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5cdd9b37f3fe4ddd9b75f500ff1b6326"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/179 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a0d697393d2c4582b5d15bbae229e836"}},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.5` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.5` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a06b9d9c489444bae884539e263004a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6d200f5c4ab043328d4c7193c5c3b557"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e55c46d2e14347c58d1315f872c216c1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"added_tokens.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"674c2130aa504a968558120c0f723778"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/502 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6b13fc0cea1543b7adc9f4693b8ae1bf"}},"metadata":{}},{"name":"stderr","text":"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Create a copy of the DataFrame\ndf_copy = df.copy()\ndf_copy['pred_response'] = None\n\n# Iterate through each row in the DataFrame with tqdm for progress visualization\nfor i in tqdm(df_copy.index, desc=\"Generating Predictions\", total=len(df_copy)):\n    \n    template = f\"Instruction:\\n{prompt}\\nINPUTDATA:{df_copy.loc[i,'context']}\\nResponse:\\n\"\n    model_input = tokenizer(template, return_tensors=\"pt\").to('cuda')\n    pred_reponse = tokenizer.decode(model.generate(**model_input, max_new_tokens=5000,temperature=0.001,top_p=0.95, repetition_penalty=1.15)[0], skip_special_tokens=True)\n    # Update the 'pred_response' column with the generated prediction\n    df_copy.loc[i,'pred_response'] = pred_reponse.replace(template,'')","metadata":{"execution":{"iopub.status.busy":"2024-04-07T13:12:44.902995Z","iopub.execute_input":"2024-04-07T13:12:44.903752Z","iopub.status.idle":"2024-04-07T15:04:14.116818Z","shell.execute_reply.started":"2024-04-07T13:12:44.903712Z","shell.execute_reply":"2024-04-07T15:04:14.115735Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stderr","text":"Generating Predictions:   0%|          | 0/51 [00:00<?, ?it/s]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\n2024-04-07 13:12:57.708080: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-04-07 13:12:57.708221: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-04-07 13:12:57.987506: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nGenerating Predictions:   2%|▏         | 1/51 [01:12<1:00:00, 72.01s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:   4%|▍         | 2/51 [02:48<1:10:39, 86.52s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:   6%|▌         | 3/51 [04:08<1:06:45, 83.45s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:   8%|▊         | 4/51 [04:52<53:12, 67.92s/it]  /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  10%|▉         | 5/51 [06:04<53:03, 69.20s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  12%|█▏        | 6/51 [08:01<1:04:14, 85.66s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  14%|█▎        | 7/51 [09:26<1:02:42, 85.52s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  16%|█▌        | 8/51 [11:00<1:03:04, 88.01s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  18%|█▊        | 9/51 [14:05<1:22:57, 118.51s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  20%|█▉        | 10/51 [22:02<2:36:37, 229.20s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  22%|██▏       | 11/51 [24:31<2:16:26, 204.65s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  24%|██▎       | 12/51 [26:12<1:52:33, 173.16s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  25%|██▌       | 13/51 [26:59<1:25:19, 134.73s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  27%|██▋       | 14/51 [34:52<2:26:09, 237.02s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  29%|██▉       | 15/51 [35:45<1:48:49, 181.38s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  31%|███▏      | 16/51 [37:27<1:31:53, 157.54s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  33%|███▎      | 17/51 [38:40<1:14:57, 132.27s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  35%|███▌      | 18/51 [39:29<58:56, 107.15s/it]  /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  37%|███▋      | 19/51 [43:56<1:22:45, 155.18s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  39%|███▉      | 20/51 [47:43<1:31:14, 176.59s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  41%|████      | 21/51 [50:40<1:28:22, 176.76s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  43%|████▎     | 22/51 [50:56<1:02:09, 128.61s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  45%|████▌     | 23/51 [52:24<54:19, 116.43s/it]  /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  47%|████▋     | 24/51 [53:23<44:41, 99.31s/it] /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  49%|████▉     | 25/51 [56:33<54:49, 126.51s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  51%|█████     | 26/51 [56:51<39:07, 93.88s/it] /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  53%|█████▎    | 27/51 [59:59<48:47, 121.99s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  55%|█████▍    | 28/51 [1:03:13<55:02, 143.59s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  57%|█████▋    | 29/51 [1:04:55<48:07, 131.26s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  59%|█████▉    | 30/51 [1:06:54<44:37, 127.51s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  61%|██████    | 31/51 [1:07:57<36:01, 108.10s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  63%|██████▎   | 32/51 [1:09:56<35:18, 111.49s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  65%|██████▍   | 33/51 [1:12:02<34:43, 115.76s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  67%|██████▋   | 34/51 [1:12:21<24:36, 86.84s/it] /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  69%|██████▊   | 35/51 [1:13:34<22:00, 82.53s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  71%|███████   | 36/51 [1:14:25<18:18, 73.23s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  73%|███████▎  | 37/51 [1:22:18<45:02, 193.01s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  75%|███████▍  | 38/51 [1:30:13<1:00:09, 277.65s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  76%|███████▋  | 39/51 [1:31:27<43:19, 216.66s/it]  /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  78%|███████▊  | 40/51 [1:34:54<39:12, 213.84s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  80%|████████  | 41/51 [1:36:39<30:10, 181.09s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  82%|████████▏ | 42/51 [1:37:51<22:16, 148.47s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  84%|████████▍ | 43/51 [1:38:55<16:23, 122.99s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  86%|████████▋ | 44/51 [1:40:23<13:06, 112.41s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  88%|████████▊ | 45/51 [1:41:33<09:58, 99.77s/it] /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  90%|█████████ | 46/51 [1:43:32<08:47, 105.43s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  92%|█████████▏| 47/51 [1:44:50<06:29, 97.36s/it] /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  94%|█████████▍| 48/51 [1:46:34<04:58, 99.35s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  96%|█████████▌| 49/51 [1:48:31<03:29, 104.66s/it]/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions:  98%|█████████▊| 50/51 [1:49:46<01:35, 95.62s/it] /opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\nGenerating Predictions: 100%|██████████| 51/51 [1:51:29<00:00, 131.16s/it]\n","output_type":"stream"}]},{"cell_type":"code","source":"sum_percentage_of_predicted_keys = 0\nsum_percentage_of_predicted_values = 0\n\nfor i in df_copy.index:\n    \n    print(\"ID: \",i)\n    pred_dict = parse_json(df_copy.loc[i,'pred_response'])\n    true_dict = parse_json(df_copy.loc[i,'answer'])\n#     print(\"-------\"*7+\"prediction\"+\"-------\"*7)\n#     print(json.dumps(pred_dict, indent=2))\n#     print('\\n')\n#     print(\"-------\"*7+\"expected\"+\"-------\"*7)\n#     print(json.dumps(true_dict, indent=2))\n    p_p_k = Percentage_of_predicted_keys(true_dict, pred_dict)\n    p_p_v = calculate_percentage_of_values(true_dict, pred_dict)\n#     print(\"\\n\"+\"-------\"*7+\"accuracy\"+\"-------\"*7)\n    print(\"keys(%)\",p_p_k)\n    print(\"values(%)\",p_p_v)\n    sum_percentage_of_predicted_keys += p_p_k\n    sum_percentage_of_predicted_values += p_p_v","metadata":{"execution":{"iopub.status.busy":"2024-04-07T15:15:05.836811Z","iopub.execute_input":"2024-04-07T15:15:05.837756Z","iopub.status.idle":"2024-04-07T15:15:05.864292Z","shell.execute_reply.started":"2024-04-07T15:15:05.837715Z","shell.execute_reply":"2024-04-07T15:15:05.863163Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"ID:  0\nkeys(%) 1.0\nvalues(%) 1.0\nID:  1\nkeys(%) 0.6923076923076923\nvalues(%) 0.3333333333333333\nID:  2\nkeys(%) 1.0\nvalues(%) 0.9696969696969697\nID:  3\nkeys(%) 1.0\nvalues(%) 0.8333333333333334\nID:  4\nkeys(%) 0.0\nvalues(%) 0.0\nID:  5\nkeys(%) 1.0\nvalues(%) 0.8333333333333334\nID:  6\nkeys(%) 1.0\nvalues(%) 0.8333333333333334\nID:  7\nkeys(%) 1.0\nvalues(%) 0.8333333333333334\nID:  8\nkeys(%) 1.0\nvalues(%) 0.5357142857142857\nID:  9\nkeys(%) 1.0\nvalues(%) 0.2727272727272727\nID:  10\nkeys(%) 1.0\nvalues(%) 0.7890625\nID:  11\nkeys(%) 0.9375\nvalues(%) 0.7619047619047619\nID:  12\nkeys(%) 1.0\nvalues(%) 0.6666666666666666\nID:  13\nkeys(%) 1.0\nvalues(%) 0.16666666666666666\nID:  14\nkeys(%) 1.0\nvalues(%) 0.7142857142857143\nID:  15\nkeys(%) 1.0\nvalues(%) 1.0\nID:  16\nkeys(%) 1.0\nvalues(%) 0.6938775510204082\nID:  17\nkeys(%) 1.0\nvalues(%) 0.6\nID:  18\nkeys(%) 0.9811320754716981\nvalues(%) 0.6583333333333333\nID:  19\nkeys(%) 0.0\nvalues(%) 0.0\nID:  20\nkeys(%) 1.0\nvalues(%) 1.0\nID:  21\nkeys(%) 0.0\nvalues(%) 0.0\nID:  22\nkeys(%) 0.0\nvalues(%) 0.0\nID:  23\nkeys(%) 1.0\nvalues(%) 0.125\nID:  24\nkeys(%) 0.96875\nvalues(%) 0.3974358974358974\nID:  25\nkeys(%) 0.0\nvalues(%) 0.0\nID:  26\nkeys(%) 0.96875\nvalues(%) 0.5705128205128205\nID:  27\nkeys(%) 0.96875\nvalues(%) 0.46794871794871795\nID:  28\nkeys(%) 0.8666666666666667\nvalues(%) 0.2\nID:  29\nkeys(%) 0.9090909090909091\nvalues(%) 0.6\nID:  30\nkeys(%) 0.8666666666666667\nvalues(%) 0.04\nID:  31\nkeys(%) 0.9565217391304348\nvalues(%) 0.5083333333333333\nID:  32\nkeys(%) 1.0\nvalues(%) 0.6666666666666666\nID:  33\nkeys(%) 0.2727272727272727\nvalues(%) 0.0\nID:  34\nkeys(%) 1.0\nvalues(%) 0.38181818181818183\nID:  35\nkeys(%) 1.0\nvalues(%) 0.9142857142857143\nID:  36\nkeys(%) 0.0\nvalues(%) 0.0\nID:  37\nkeys(%) 0.0\nvalues(%) 0.0\nID:  38\nkeys(%) 1.0\nvalues(%) 0.5178571428571429\nID:  39\nkeys(%) 0.17647058823529413\nvalues(%) 0.0\nID:  40\nkeys(%) 1.0\nvalues(%) 0.8571428571428571\nID:  41\nkeys(%) 0.0\nvalues(%) 0.0\nID:  42\nkeys(%) 1.0\nvalues(%) 0.625\nID:  43\nkeys(%) 1.0\nvalues(%) 0.6349206349206349\nID:  44\nkeys(%) 1.0\nvalues(%) 0.9523809523809523\nID:  45\nkeys(%) 1.0\nvalues(%) 0.49122807017543857\nID:  46\nkeys(%) 0.0\nvalues(%) 0.0\nID:  47\nkeys(%) 1.0\nvalues(%) 0.5\nID:  48\nkeys(%) 0.0\nvalues(%) 0.0\nID:  49\nkeys(%) 1.0\nvalues(%) 0.4090909090909091\nID:  50\nkeys(%) 0.8947368421052632\nvalues(%) 0.34285714285714286\n","output_type":"stream"}]},{"cell_type":"code","source":"average_percentage_of_predicted_keys = sum_percentage_of_predicted_keys/len(df_copy)\naverage_percentage_of_predicted_values = sum_percentage_of_predicted_values/len(df_copy)\n\nprint(\"Average Percentage of Predicted Keys:\", average_percentage_of_predicted_keys)\nprint(\"Average Percentage of Predicted values:\", average_percentage_of_predicted_values)","metadata":{"execution":{"iopub.status.busy":"2024-04-07T15:16:00.947218Z","iopub.execute_input":"2024-04-07T15:16:00.947601Z","iopub.status.idle":"2024-04-07T15:16:00.953433Z","shell.execute_reply.started":"2024-04-07T15:16:00.947570Z","shell.execute_reply":"2024-04-07T15:16:00.952307Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"Average Percentage of Predicted Keys: 0.7541190284784685\nAverage Percentage of Predicted values: 0.4646682633354736\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id='8'></a>\n>## dolphin-2.2.1-mistral-7b","metadata":{}},{"cell_type":"code","source":"base_model_id = \"cognitivecomputations/dolphin-2.2.1-mistral-7b\"\nbnb_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_use_double_quant=True,\n    bnb_4bit_quant_type=\"nf4\",\n    bnb_4bit_compute_dtype=torch.bfloat16\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config, device_map=\"auto\",trust_remote_code=True)\ntokenizer = AutoTokenizer.from_pretrained(base_model_id, use_fast=False,device_map=\"auto\")","metadata":{"execution":{"iopub.status.busy":"2024-04-07T15:16:18.041910Z","iopub.execute_input":"2024-04-07T15:16:18.042288Z","iopub.status.idle":"2024-04-07T15:18:23.657817Z","shell.execute_reply.started":"2024-04-07T15:16:18.042260Z","shell.execute_reply":"2024-04-07T15:18:23.656763Z"},"trusted":true},"execution_count":69,"outputs":[{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/618 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7ad6e44b2cf84fa08834828de3cba855"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json:   0%|          | 0.00/25.1k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cbdfbfd1a65140e9a37e03c0073a2f4e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5d7e2327231049ba9ac98fbb3409e910"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00001-of-00002.safetensors:   0%|          | 0.00/9.94G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3972b4e1544542e4a7faf9a83e14dd19"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00002-of-00002.safetensors:   0%|          | 0.00/4.54G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8cf8c7e0fe2447bda8ca6118eb6b3823"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"997e0676d27a4ac5a17c7e1d138334a6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/114 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8981adb7c0264cf2ade5f2f494cd01c8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/1.70k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d0084fe41afd4f059e9c2f1dea2d6837"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d85128c15f4e45b2a450e94d66bccfab"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"added_tokens.json:   0%|          | 0.00/51.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f2cd4efb24df4b7ebad4207475562029"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/443 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a33322574995425684a82c345011ce0c"}},"metadata":{}},{"name":"stderr","text":"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Create a copy of the DataFrame\ndf_copy3 = df.copy()\ndf_copy3['pred_response'] = None\n\n# Iterate through each row in the DataFrame with tqdm for progress visualization\nfor i in tqdm(df_copy3.index, desc=\"Generating Predictions\", total=len(df_copy3)):\n    \n    template = f\"Instruction:\\n{prompt}\\nINPUTDATA:{df_copy3.loc[i,'context']}\\nResponse:\\n\"\n    inputs = tokenizer(template, return_tensors=\"pt\").to(model.device) \n    outputs = model.generate(**inputs, use_cache=True,max_length=4096)\n    output_text = tokenizer.decode(outputs[0]) \n    df_copy3.loc[i,'pred_response'] = output_text.replace(template,'')","metadata":{"execution":{"iopub.status.busy":"2024-04-07T15:18:23.659967Z","iopub.execute_input":"2024-04-07T15:18:23.660408Z","iopub.status.idle":"2024-04-07T16:42:51.111024Z","shell.execute_reply.started":"2024-04-07T15:18:23.660368Z","shell.execute_reply":"2024-04-07T16:42:51.109864Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stderr","text":"Generating Predictions:   0%|          | 0/51 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:   2%|▏         | 1/51 [00:54<45:09, 54.18s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:   4%|▍         | 2/51 [02:15<57:20, 70.21s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:   6%|▌         | 3/51 [03:35<59:45, 74.71s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:   8%|▊         | 4/51 [04:19<48:50, 62.34s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  10%|▉         | 5/51 [06:54<1:13:23, 95.74s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  12%|█▏        | 6/51 [08:53<1:17:45, 103.67s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  14%|█▎        | 7/51 [10:18<1:11:39, 97.71s/it] Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  16%|█▌        | 8/51 [11:06<58:36, 81.78s/it]  Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  18%|█▊        | 9/51 [14:12<1:20:05, 114.41s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  20%|█▉        | 10/51 [15:48<1:14:19, 108.78s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  22%|██▏       | 11/51 [18:15<1:20:15, 120.39s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  24%|██▎       | 12/51 [19:56<1:14:29, 114.61s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  25%|██▌       | 13/51 [20:42<59:27, 93.87s/it]   Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  27%|██▋       | 14/51 [21:45<52:04, 84.44s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  29%|██▉       | 15/51 [22:36<44:34, 74.29s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  31%|███▏      | 16/51 [23:23<38:35, 66.16s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  33%|███▎      | 17/51 [24:36<38:37, 68.15s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  35%|███▌      | 18/51 [25:24<34:10, 62.14s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  37%|███▋      | 19/51 [28:18<51:03, 95.74s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  39%|███▉      | 20/51 [31:08<1:01:02, 118.14s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  43%|████▎     | 22/51 [36:34<1:07:06, 138.85s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  45%|████▌     | 23/51 [38:22<1:00:27, 129.55s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  47%|████▋     | 24/51 [39:22<48:52, 108.61s/it]  Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  49%|████▉     | 25/51 [42:27<57:01, 131.60s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  51%|█████     | 26/51 [45:28<1:00:59, 146.40s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  53%|█████▎    | 27/51 [48:31<1:02:51, 157.17s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  55%|█████▍    | 28/51 [51:35<1:03:20, 165.22s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  57%|█████▋    | 29/51 [53:10<52:52, 144.19s/it]  Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  59%|█████▉    | 30/51 [55:15<48:29, 138.54s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  61%|██████    | 31/51 [56:10<37:47, 113.37s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  63%|██████▎   | 32/51 [58:09<36:30, 115.27s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  65%|██████▍   | 33/51 [1:00:17<35:43, 119.11s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  67%|██████▋   | 34/51 [1:00:57<26:58, 95.22s/it] Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  69%|██████▊   | 35/51 [1:02:03<23:03, 86.48s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  71%|███████   | 36/51 [1:02:55<19:00, 76.03s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  73%|███████▎  | 37/51 [1:03:46<16:00, 68.61s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  75%|███████▍  | 38/51 [1:04:35<13:36, 62.80s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  76%|███████▋  | 39/51 [1:05:49<13:11, 65.98s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  78%|███████▊  | 40/51 [1:08:17<16:38, 90.77s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  80%|████████  | 41/51 [1:09:11<13:16, 79.63s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  82%|████████▏ | 42/51 [1:10:01<10:36, 70.74s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  84%|████████▍ | 43/51 [1:11:04<09:08, 68.50s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  86%|████████▋ | 44/51 [1:12:31<08:38, 74.11s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  88%|████████▊ | 45/51 [1:13:43<07:19, 73.27s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  90%|█████████ | 46/51 [1:15:46<07:20, 88.17s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  92%|█████████▏| 47/51 [1:17:57<06:45, 101.30s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  94%|█████████▍| 48/51 [1:19:45<05:09, 103.15s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  96%|█████████▌| 49/51 [1:21:26<03:24, 102.39s/it]Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions:  98%|█████████▊| 50/51 [1:22:44<01:35, 95.24s/it] Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.\nGenerating Predictions: 100%|██████████| 51/51 [1:24:27<00:00, 99.36s/it]\n","output_type":"stream"}]},{"cell_type":"code","source":"sum_percentage_of_predicted_keys = 0\nsum_percentage_of_predicted_values = 0\n\nfor i in df_copy3.index:\n    \n    print(\"ID: \",i)\n    pred_dict = parse_json(df_copy3.loc[i,'pred_response'])\n    true_dict = parse_json(df_copy3.loc[i,'answer'])\n#     print(\"-------\"*7+\"prediction\"+\"-------\"*7)\n#     print(json.dumps(pred_dict, indent=2))\n#     print('\\n')\n#     print(\"-------\"*7+\"expected\"+\"-------\"*7)\n#     print(json.dumps(true_dict, indent=2))\n    p_p_k = Percentage_of_predicted_keys(true_dict, pred_dict)\n    p_p_v = calculate_percentage_of_values(true_dict, pred_dict)\n#     print(\"\\n\"+\"-------\"*7+\"accuracy\"+\"-------\"*7)\n    print(\"keys(%)\",p_p_k)\n    print(\"values(%)\",p_p_v)\n    sum_percentage_of_predicted_keys += p_p_k\n    sum_percentage_of_predicted_values += p_p_v","metadata":{"execution":{"iopub.status.busy":"2024-04-07T16:49:38.399757Z","iopub.execute_input":"2024-04-07T16:49:38.400162Z","iopub.status.idle":"2024-04-07T16:49:38.429324Z","shell.execute_reply.started":"2024-04-07T16:49:38.400130Z","shell.execute_reply":"2024-04-07T16:49:38.428288Z"},"trusted":true},"execution_count":79,"outputs":[{"name":"stdout","text":"ID:  0\nkeys(%) 1.0\nvalues(%) 1.0\nID:  1\nkeys(%) 0.9230769230769231\nvalues(%) 0.8333333333333334\nID:  2\nkeys(%) 1.0\nvalues(%) 1.0\nID:  3\nkeys(%) 1.0\nvalues(%) 0.8333333333333334\nID:  4\nkeys(%) 1.0\nvalues(%) 0.8712121212121212\nID:  5\nkeys(%) 1.0\nvalues(%) 0.8333333333333334\nID:  6\nkeys(%) 1.0\nvalues(%) 0.8194444444444444\nID:  7\nkeys(%) 1.0\nvalues(%) 0.8333333333333334\nID:  8\nkeys(%) 1.0\nvalues(%) 0.5535714285714286\nID:  9\nkeys(%) 1.0\nvalues(%) 0.26136363636363635\nID:  10\nkeys(%) 1.0\nvalues(%) 0.84375\nID:  11\nkeys(%) 0.9375\nvalues(%) 0.8253968253968254\nID:  12\nkeys(%) 1.0\nvalues(%) 0.6666666666666666\nID:  13\nkeys(%) 1.0\nvalues(%) 0.16666666666666666\nID:  14\nkeys(%) 1.0\nvalues(%) 0.7142857142857143\nID:  15\nkeys(%) 0.6363636363636364\nvalues(%) 0.2\nID:  16\nkeys(%) 1.0\nvalues(%) 0.7142857142857143\nID:  17\nkeys(%) 1.0\nvalues(%) 0.6\nID:  18\nkeys(%) 1.0\nvalues(%) 0.8\nID:  19\nkeys(%) 1.0\nvalues(%) 0.7142857142857143\nID:  20\nkeys(%) 1.0\nvalues(%) 0.9492753623188406\nID:  21\nkeys(%) 0.0\nvalues(%) 0.0\nID:  22\nkeys(%) 0.9047619047619048\nvalues(%) 0.09090909090909091\nID:  23\nkeys(%) 1.0\nvalues(%) 0.125\nID:  24\nkeys(%) 1.0\nvalues(%) 0.6794871794871795\nID:  25\nkeys(%) 1.0\nvalues(%) 0.6666666666666666\nID:  26\nkeys(%) 1.0\nvalues(%) 0.717948717948718\nID:  27\nkeys(%) 1.0\nvalues(%) 0.7628205128205128\nID:  28\nkeys(%) 0.8\nvalues(%) 0.4\nID:  29\nkeys(%) 0.9090909090909091\nvalues(%) 0.6\nID:  30\nkeys(%) 0.8666666666666667\nvalues(%) 0.22\nID:  31\nkeys(%) 1.0\nvalues(%) 0.65\nID:  32\nkeys(%) 1.0\nvalues(%) 0.6666666666666666\nID:  33\nkeys(%) 0.8181818181818182\nvalues(%) 0.16666666666666666\nID:  34\nkeys(%) 1.0\nvalues(%) 0.4\nID:  35\nkeys(%) 1.0\nvalues(%) 1.0\nID:  36\nkeys(%) 1.0\nvalues(%) 0.9714285714285714\nID:  37\nkeys(%) 0.9230769230769231\nvalues(%) 0.25\nID:  38\nkeys(%) 1.0\nvalues(%) 0.7142857142857143\nID:  39\nkeys(%) 1.0\nvalues(%) 0.6666666666666666\nID:  40\nkeys(%) 1.0\nvalues(%) 0.7714285714285715\nID:  41\nkeys(%) 0.9230769230769231\nvalues(%) 0.375\nID:  42\nkeys(%) 1.0\nvalues(%) 0.6041666666666666\nID:  43\nkeys(%) 1.0\nvalues(%) 0.6507936507936508\nID:  44\nkeys(%) 1.0\nvalues(%) 1.0\nID:  45\nkeys(%) 1.0\nvalues(%) 0.5\nID:  46\nkeys(%) 1.0\nvalues(%) 0.45614035087719296\nID:  47\nkeys(%) 1.0\nvalues(%) 0.5\nID:  48\nkeys(%) 0.9047619047619048\nvalues(%) 0.375\nID:  49\nkeys(%) 1.0\nvalues(%) 0.5\nID:  50\nkeys(%) 0.8947368421052632\nvalues(%) 0.32857142857142857\n","output_type":"stream"}]},{"cell_type":"code","source":"average_percentage_of_predicted_keys = sum_percentage_of_predicted_keys/len(df_copy)\naverage_percentage_of_predicted_values = sum_percentage_of_predicted_values/len(df_copy)\n\nprint(\"Average Percentage of Predicted Keys:\", average_percentage_of_predicted_keys)\nprint(\"Average Percentage of Predicted values:\", average_percentage_of_predicted_values)","metadata":{"execution":{"iopub.status.busy":"2024-04-07T16:50:02.532514Z","iopub.execute_input":"2024-04-07T16:50:02.532928Z","iopub.status.idle":"2024-04-07T16:50:02.538980Z","shell.execute_reply.started":"2024-04-07T16:50:02.532892Z","shell.execute_reply":"2024-04-07T16:50:02.537955Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"Average Percentage of Predicted Keys: 0.9498293029639778\nAverage Percentage of Predicted values: 0.6047683284257857\n","output_type":"stream"}]}]}